{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.143125\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 10.217197 analytic: 10.221308, relative error: 2.011392e-04\n",
      "numerical: -27.057742 analytic: -27.057742, relative error: 7.642583e-12\n",
      "numerical: 23.925981 analytic: 23.925981, relative error: 1.818380e-12\n",
      "numerical: 1.174817 analytic: 1.174817, relative error: 1.540851e-11\n",
      "numerical: -8.204964 analytic: -8.204964, relative error: 1.183161e-13\n",
      "numerical: 19.281271 analytic: 19.281271, relative error: 3.778932e-12\n",
      "numerical: -0.457547 analytic: -0.457547, relative error: 1.123318e-10\n",
      "numerical: -11.725785 analytic: -11.725785, relative error: 1.969056e-11\n",
      "numerical: -37.183446 analytic: -37.183446, relative error: 5.653814e-12\n",
      "numerical: -5.315847 analytic: -5.315847, relative error: 7.449235e-11\n",
      "numerical: -14.394450 analytic: -14.394450, relative error: 6.390316e-12\n",
      "numerical: -3.464012 analytic: -3.464012, relative error: 3.355342e-11\n",
      "numerical: -14.339979 analytic: -14.339979, relative error: 1.949598e-11\n",
      "numerical: 7.990133 analytic: 7.990133, relative error: 4.833945e-11\n",
      "numerical: -12.509091 analytic: -12.509091, relative error: 1.514032e-11\n",
      "numerical: 30.501850 analytic: 30.501850, relative error: 2.794826e-12\n",
      "numerical: 8.398984 analytic: 8.398984, relative error: 2.739421e-11\n",
      "numerical: -8.337256 analytic: -8.337256, relative error: 1.541294e-12\n",
      "numerical: -9.980571 analytic: -9.980571, relative error: 7.347511e-11\n",
      "numerical: 10.334734 analytic: 10.334734, relative error: 4.015598e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ 合页函数在转折点不可微 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.143125e+00 computed in 0.141523s\n",
      "Vectorized loss: 9.143125e+00 computed in 0.005892s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.147941s\n",
      "Vectorized loss and gradient: computed in 0.005571s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 784.555012\n",
      "iteration 100 / 1500: loss 285.638101\n",
      "iteration 200 / 1500: loss 107.534648\n",
      "iteration 300 / 1500: loss 41.921245\n",
      "iteration 400 / 1500: loss 18.932325\n",
      "iteration 500 / 1500: loss 10.153897\n",
      "iteration 600 / 1500: loss 6.904113\n",
      "iteration 700 / 1500: loss 6.506918\n",
      "iteration 800 / 1500: loss 5.835823\n",
      "iteration 900 / 1500: loss 5.794805\n",
      "iteration 1000 / 1500: loss 5.402586\n",
      "iteration 1100 / 1500: loss 5.743682\n",
      "iteration 1200 / 1500: loss 5.062908\n",
      "iteration 1300 / 1500: loss 4.966499\n",
      "iteration 1400 / 1500: loss 5.176222\n",
      "That took 3.303836s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.373469\n",
      "validation accuracy: 0.389000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/victorbian/Desktop/CS231n/assignment1/cs231n/classifiers/linear_svm.py:101: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss = np.sum(scores) / num_train + reg*np.sum(W*W)\n",
      "/home/victorbian/.local/lib/python3.7/site-packages/numpy/core/fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "/home/victorbian/Desktop/CS231n/assignment1/cs231n/classifiers/linear_svm.py:101: RuntimeWarning: overflow encountered in multiply\n",
      "  loss = np.sum(scores) / num_train + reg*np.sum(W*W)\n",
      "/home/victorbian/Desktop/CS231n/assignment1/cs231n/classifiers/linear_svm.py:119: RuntimeWarning: overflow encountered in multiply\n",
      "  dW = np.dot(X.T, scores)/num_train + W*reg*2\n",
      "/home/victorbian/Desktop/CS231n/assignment1/cs231n/classifiers/linear_svm.py:99: RuntimeWarning: invalid value encountered in less\n",
      "  scores[scores<0] = 0\n",
      "/home/victorbian/Desktop/CS231n/assignment1/cs231n/classifiers/linear_svm.py:116: RuntimeWarning: invalid value encountered in greater\n",
      "  scores[scores>0] = 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.364041 val accuracy: 0.376000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.352898 val accuracy: 0.378000\n",
      "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.062184 val accuracy: 0.070000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.378000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "#Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train, y_train, learning_rate=lr, reg=reg,\n",
    "                      num_iters=1500, batch_size=200, verbose=False)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        acc_train = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        acc_val = np.mean(y_val == y_val_pred)\n",
    "        results[(lr, reg)] = (acc_train, acc_val)\n",
    "        if acc_val > best_val:\n",
    "            best_svm = svm\n",
    "            best_val = acc_val\n",
    "        \n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.358000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
